1. Field of the Invention
This invention relates to image and video signal transmission, and in particular, it relates to a method of transmitting image and video signals with adjustable resolution and supporting a TV wall mode using multiresolution decomposition technique.
2. Description of the Related Art
Discrete wavelet transform is a technique often used in image analysis. The principle of discrete wavelet transform, as elaborated by a number of papers, is to hierarchically decompose an input signal into a series of lower resolution smooth signal and their associated detail signals. The decomposition is repeated for a number of levels; at each level, the smooth signal is decomposed into a smooth signal (contains most of the energy in the image at that level) and a number of detail signals at the next level (which generally contain relatively little energy). At each level, the smooth signal and the number of detail signals collectively contain the information needed to completely reconstruct the smooth signal at the next higher resolution level. See, for example, A. Grossmann and J. Morlet, “Decomposition of Hardy function into square integrable wavelets of constant shape,” SIAM J. Math. Anal., Vol. 15, pp. 723˜736, 1984; I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math., Vol. 41, pp. 909˜996, 1988; S. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Patt Anal. Machine Intell., Vol. 7, pp. 674˜693, 1989; and G. T. STRANG, “Wavelets and dilation equations: A brief introduction,” SIAM Rev., Vol. 31, pp. 614˜627, 1989. This technique is also referred to as multiresolution decomposition. There are many different implementations of multiresolution decomposition by using different types of wavelet filter banks, such as 9/7 tap filter bank, D4 filter bank, Haar filter bank, triangular-mesh based image filter bank, etc. The type of filter bank is determined by the scalar function and the wavelet function that are used. See, for example, M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, “Image coding using wavelet transform,” IEEE Trans. Image Processing, Vol. 1, no. 2, 1992; I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Commun. Pure Appl. Math., Vol. 41, pp. 909˜996, 1988; M. G. Albanesi and I. Delotto, “Image compression by the wavelet decomposition,” Signal Processing, Vol. 3, no. 3, pp. 265˜274, 1992; and Wenshun Li and Jiegu Li, “Image Compression Using Multiresolution Decomposition of Triangular Mesh Model,” Acta Automatica Sinica, 1999 25 (05): 613-619.
FIG. 1 schematically illustrates an example of how an image is decomposed into lower resolution signals by a three-level multiresolution decomposition. The original image 10 is decomposed into four first resolution level signals 11, indicated here as LL1, LH1, HL1 and HH1, by applying low-pass and high-pass filters to the original image. LL1 is the first-level smooth signal, while LH1, HL1 and HH1 are first-level detail signals. The smooth signal LL1 is generated by applying a low-pass filter to the original image in both the horizontal and vertical directions; the detail signal LH1 is generated by applying a low-pass filter in the horizontal direction and a high-pass filter in the vertical direction; the detail signal HL1 is generated by applying high-pass filter in the horizontal direction and a low pass-filter in the vertical direction; and the detail signal HH1 is generated by applying a high-pass filter in both the horizontal and vertical directions. The first-level smooth signal LL1 has a lower spatial resolution, i.e., having fewer pixels, than the original image. The original image 10 can be completely reconstructed from the four first-level signals LL1, LH1, HL1 and HH1. The first-level smooth signal LL1 is in turn decomposed into four second resolution level signals 12, indicated here as smooth signal LL2 and detail signals LH2, HL2 and HH2. The first-level smooth signal LL1 can be completely reconstructed from the four second-level signals LL2, LH2, HL2 and HH2. The second-level smooth signal LL2 is further decomposed into four third resolution level signals 13. More levels of decomposition can be similarly carried out. FIG. 2 illustrates a two-dimensional image signal 21 and the four lower resolution level signals 22A-D resulting from one level of decomposition, including one smooth signal 22A, one horizontal signal 22B, one vertical signal 22C, and one diagonal direction signal 22D. It should be noted that in this example, the three detailed signals 22B-D are in fact negative or inverted images (i.e., black background with white images), but for purposes of illustration, they are shown in FIG. 2 as positive images.
When the multiresolution decomposition process is finished, the resulting signals include one smooth signal of the lowest resolution level (i.e. the nth-level), and the detail signals of all resolution levels. In this respect, note that while FIG. 1 shows the higher resolution level smooth signals LL1 and LL2, they are in fact not present in the final resulting signals of the multiresolution decomposition. When reconstructing the original image, the lowest resolution level smooth signal (e.g. LL3 in this example) and the lowest resolution level detail signals (LH3, HL3 and HH3 in this example) are first used to construct the next higher resolution level smooth signal (LL2 in this example). The reconstructed smooth signal of that level (LL2) and the detail signals of the same level (LH2, HL2 and HH2 in this example) are used to reconstruct the smooth signal of one level above (LL1 in this example), and so on, until the original image is reconstructed.